Solution of an inverse adsorption problem with an epidemic genetic algorithm and the generalized extremal optimization algorithm

Abstract

In the present work two recently developed stochastic methods, the epidemic genetic algorithm and the generalized extremal optimization algorithm are used for the solution of an inverse mass transfer problem, which is implicitly formulated as an optimization problem, for the estimation of parameters associated with the adsorption of biomolecules in resin beds. The estimates obtained with both methods present good accuracy, even in the presence of noisy data, provided that the model and experiment used are sensitive to the parameters being estimated. With Thomas' model for the direct mass transfer problem and real experimental data for lisozyme in adsorption columns, it is possible to estimate the maximum adsorption capacity in Langmuir's adsorption isotherm.

Application of Simulated Annealing and Hybrid Methods in the Solution of Inverse Heat and Mass Transfer Problems

“…The solution of inverse radiative transfer problems has been obtained by using different methodologies, namely deterministic, stochastic and hybrid methods. As examples of techniques developed for dealing with inverse radiative transfer problems, the following methods can be cited: LevenbergMarquardt method (Silva Neto and Moura Neto, 2005); Simulated Annealing (Silva Neto and Soeiro, 2002;Souza et al, 2007); Genetic Algorithms (Silva Neto and Soeiro, 2002;Souza et al, 2007); Artificial Neural Networks (Soeiro et al, 2004); Simulated Annealing and Levenberg-Marquard (Silva Neto and Soeiro, 2006); Ant Colony Optimization (Souto et al, 2005); Particle Swarm Optimization (Becceneri et al, 2006); Generalized Extremal Optimization (Souza et al, 2007); Interior Points Method (Silva ; Particle Collision Algorithm (Knupp et al, 2007); Artificial Neural Networks and Monte Carlo Method (Chalhoub et al, 2007b); Epidemic Genetic Algorithm and the Generalized Extremal Optimization Algorithm (Cuco et al, 2009); Generalized Extremal Optimization and Simulated Annealing Algorithm (Galski et al, 2009); Hybrid Approach with Artificial Neural Networks, Levenberg-Marquardt and Simulated Annealing Methods (Lugon, Silva Neto and Santana, 2009;Lugon and Silva Neto, 2010), Differential Evolution (Lobato et al, 2008;Lobato et al, 2009), Differential Evolution and Simulated Annealing Methods (Lobato et al, 2010). In this chapter we first describe three problems of heat and mass transfer, followed by the formulation of the inverse problems, the description of the solution of the inverse problems with Simulated Annealing and its hybridization with other methods, and some test case results.…”

A comparative study of the application of differential evolution and simulated annealing in radiative transfer problems

“…Technical Editor: Amir de Oliveira Júnior Genetic Algorithms (Silva Neto and Soeiro, 2002;Souza et al, 2007); Artificial Neural Networks (Soeiro et al, 2004;Oliveira et al, 2010); Ant Colony Optimization (Souto et al, 2005); Particle Swarm Optimization (Becceneri et al, 2006); Generalized Extremal Optimization (Souza et al, 2007); Interior Points Method (Silva Neto and Silva Neto, 2003); Particle Collision Algorithm (Knupp et al, 2007); Monte Carlo Method and Three Variations of the Discrete Ordinates Method (Chalhoub et al, 2007a); Artificial Neural Networks and Monte Carlo Method (Chalhoub et al, 2007b); Epidemic Genetic Algorithm and the Generalized Extremal Optimization Algorithm (Cuco et al, 2009); Epidemic Genetic Algorithm and Simulated Annealing Algorithm (Galski et al, 2009); Hybrid Approach with Artificial Neural Networks, LevenbergMarquardt and Simulated Annealing Methods (Lugon et al, 2009).…”

Neural network model and isotherm study for removal of phenol from aqueous solution by orange peel ash

“…For fine tuning and predicting the adsorption mechanism, many software-based models have been used (Carsky and Do 1999;Cuco et al 2009). In recent years, ANN has become a popular choice among engineers and scientists as one of the powerful tools for predicting contamination and concentration of different effluents and chemicals in drinking water, wastewater and aquifers and energy content in municipal solid waste (Ogwueleka and Ogwueleka 2010).…”